<p>
    Run inference on a PyTorch model. Given an input tensor and a trained <code>torch.nn.Linear</code> model, compute the forward pass and store the result in the output tensor.
  </p>
  
  <p>
    The model performs a linear transformation: <code>output = input @ weight.T + bias</code> where <code>weight</code> has shape [output_size, input_size] and <code>bias</code> has shape [output_size].
  </p>
  
  <h2>Implementation Requirements</h2>
  <ul>
    <li>Use PyTorch's built-in functions and operations</li>
    <li>The <code>solve</code> function signature must remain unchanged</li>
    <li>The final result must be stored in the <code>output</code> tensor</li>
    <li>The model is already loaded and ready for inference</li>
  </ul>
  
  <h2>Example 1:</h2>
  <pre>
  Input:  input = [[1.0, 2.0]]  (batch_size=1, input_size=2)
          model: Linear layer with weight=[[0.5, 1.0], [1.5, 0.5]], bias=[0.1, 0.2]
  Output: output = [[2.6, 2.7]]  (batch_size=1, output_size=2)
  </pre>
  
  <h2>Example 2:</h2>
  <pre>
  Input:  input = [[1.0], [2.0], [3.0]]  (batch_size=3, input_size=1)
          model: Linear layer with weight=[[2.0]], bias=[1.0]
  Output: output = [[3.0], [5.0], [7.0]]  (batch_size=3, output_size=1)
  </pre>
  
  <h2>Constraints</h2>
  <ul>
    <li>1 ≤ <code>batch_size</code> ≤ 1,000</li>
    <li>1 ≤ <code>input_size</code> ≤ 1,000</li>
    <li>1 ≤ <code>output_size</code> ≤ 1,000</li>
    <li>-10.0 ≤ input values ≤ 10.0</li>
  </ul>